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» Using Problems to Learn Service-Oriented Computing
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ICML
2005
IEEE
16 years 5 months ago
Learning as search optimization: approximate large margin methods for structured prediction
Mappings to structured output spaces (strings, trees, partitions, etc.) are typically learned using extensions of classification algorithms to simple graphical structures (eg., li...
Daniel Marcu, Hal Daumé III
135
Voted
WIDM
2004
ACM
15 years 10 months ago
A comprehensive solution to the XML-to-relational mapping problem
The use of relational database management systems (RDBMSs) to store and query XML data has attracted considerable interest with a view to leveraging their powerful and reliable da...
Sihem Amer-Yahia, Fang Du, Juliana Freire
COLT
1993
Springer
15 years 8 months ago
Parameterized Learning Complexity
We describe three applications in computational learning theory of techniques and ideas recently introduced in the study of parameterized computational complexity. (1) Using param...
Rodney G. Downey, Patricia A. Evans, Michael R. Fe...
ICDM
2009
IEEE
172views Data Mining» more  ICDM 2009»
15 years 11 months ago
Sparse Least-Squares Methods in the Parallel Machine Learning (PML) Framework
—We describe parallel methods for solving large-scale, high-dimensional, sparse least-squares problems that arise in machine learning applications such as document classificatio...
Ramesh Natarajan, Vikas Sindhwani, Shirish Tatikon...
ACMSE
2008
ACM
15 years 6 months ago
Optimization of the multiple retailer supply chain management problem
With stock surpluses and shortages representing one of the greatest elements of risk to wholesalers, a solution to the multiretailer supply chain management problem would result i...
Caio Soares, Gerry V. Dozier, Emmett Lodree, Jared...